While content is pervasive in any AMS/CMS/LMS, however the method of extracting insights about reusability/togetherness of content is not available in existing LMS/CMS/AMS. What is also missing is a method/technique to predict the degree of reuse or togetherness of the content in future usages. Currently systems exist where content categorization and searching is done in more of a streamlined fashion; however the reusability study and predictive study has not been done so far on these systems. On top of it, there does not exist any system/protocol through which such reuse/togetherness data can be exchanged between two AMS/CMS/LMSs.
In a related existing solution there is a description of a method for tracing variation of concept knowledge of learners over time and evaluating content organization of learning resources used by the learners. The method jointly traces latent learner concept knowledge and simultaneously estimates the quality and content organization of the corresponding learning resources (such as textbook sections or lecture videos), and the questions in assessment sets. The prior art further discloses a block multi-convex optimization-based algorithms that estimate all the learner concept knowledge state transition parameters of learning resources and question-concept associations and their intrinsic difficulties. This prior work mainly focuses on the course content evolutions over time after analyzing learners' outcomes.
Another prior art discloses a method for analyzing, querying, and mining graph databases using sub graph and similarity querying. The prior art describes a tree-based index called Closure-tree, or C-tree. Each node in the tree contains discriminative information about its descendants in order to facilitate effective pruning. This summary information is represented as a graph closure, a “bounding box” of the structural information of the constituent graphs. The C-tree supports both subgraph queries and similarity queries on various kinds of graphs. The graph closures capture the entire structure of constituent graphs, which implies high pruning rates.
Another solution provides a relationship graph of nodes representing entities and edges representing relationships among the entities is searched to find a match on search criteria, such as an individual or a company. This solution uses a graph database as it provides best performance for such type of analytics.
Another solution provides an e-learning system and methodology structures content so that the content is reusable and flexible. For example, the content structure allows the creator of a course to reuse existing content to create new or additional courses. In addition, the content structure provides flexible content delivery that may be adapted to the learning styles of different learners. This solution focusses on finding out the association between a learning objective and their associate content;